Overview

Dataset statistics

Number of variables13
Number of observations2968
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory301.6 KiB
Average record size in memory104.0 B

Variable types

Numeric13

Warnings

gross_revenue is highly correlated with qtde_itemsHigh correlation
qtde_items is highly correlated with gross_revenueHigh correlation
avg_ticket is highly skewed (γ1 = 25.1569664) Skewed
frequency is highly skewed (γ1 = 24.87687084) Skewed
qtde_returns is highly skewed (γ1 = 21.9754032) Skewed
df_index has unique values Unique
customer_id has unique values Unique
avg_ticket has unique values Unique
recency_days has 33 (1.1%) zeros Zeros
qtde_returns has 1481 (49.9%) zeros Zeros

Reproduction

Analysis started2021-06-09 00:08:31.438107
Analysis finished2021-06-09 00:08:55.272694
Duration23.83 seconds
Software versionpandas-profiling v2.13.0
Download configurationconfig.yaml

Variables

df_index
Real number (ℝ≥0)

UNIQUE

Distinct2968
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2316.666442
Minimum0
Maximum5714
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-06-08T21:08:55.395568image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile185.35
Q1928.5
median2119.5
Q33536.25
95-th percentile5034.3
Maximum5714
Range5714
Interquartile range (IQR)2607.75

Descriptive statistics

Standard deviation1554.722712
Coefficient of variation (CV)0.6711033938
Kurtosis-1.010637904
Mean2316.666442
Median Absolute Deviation (MAD)1270.5
Skewness0.3426249769
Sum6875866
Variance2417162.71
MonotonicityStrictly increasing
2021-06-08T21:08:55.531361image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
26701
 
< 0.1%
26581
 
< 0.1%
45641
 
< 0.1%
26601
 
< 0.1%
6131
 
< 0.1%
26621
 
< 0.1%
6151
 
< 0.1%
48241
 
< 0.1%
6191
 
< 0.1%
Other values (2958)2958
99.7%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
ValueCountFrequency (%)
57141
< 0.1%
56951
< 0.1%
56851
< 0.1%
56791
< 0.1%
56581
< 0.1%

customer_id
Real number (ℝ≥0)

UNIQUE

Distinct2968
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15270.37702
Minimum12347
Maximum18287
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-06-08T21:08:55.658466image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum12347
5-th percentile12619.35
Q113798.75
median15220.5
Q316768.5
95-th percentile17964.65
Maximum18287
Range5940
Interquartile range (IQR)2969.75

Descriptive statistics

Standard deviation1719.144523
Coefficient of variation (CV)0.1125803587
Kurtosis-1.206178196
Mean15270.37702
Median Absolute Deviation (MAD)1489
Skewness0.03219371129
Sum45322479
Variance2955457.892
MonotonicityNot monotonic
2021-06-08T21:08:55.786078image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
163841
 
< 0.1%
181641
 
< 0.1%
129331
 
< 0.1%
129351
 
< 0.1%
149841
 
< 0.1%
170331
 
< 0.1%
137041
 
< 0.1%
129391
 
< 0.1%
170371
 
< 0.1%
141251
 
< 0.1%
Other values (2958)2958
99.7%
ValueCountFrequency (%)
123471
< 0.1%
123481
< 0.1%
123521
< 0.1%
123561
< 0.1%
123581
< 0.1%
ValueCountFrequency (%)
182871
< 0.1%
182831
< 0.1%
182821
< 0.1%
182771
< 0.1%
182761
< 0.1%

gross_revenue
Real number (ℝ≥0)

HIGH CORRELATION

Distinct2962
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2693.485061
Minimum6.2
Maximum279138.02
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-06-08T21:08:55.916855image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum6.2
5-th percentile229.7325
Q1570.845
median1085.51
Q32306.905
95-th percentile7169.562
Maximum279138.02
Range279131.82
Interquartile range (IQR)1736.06

Descriptive statistics

Standard deviation10135.46528
Coefficient of variation (CV)3.762955818
Kurtosis397.3013221
Mean2693.485061
Median Absolute Deviation (MAD)670.84
Skewness17.63537227
Sum7994263.66
Variance102727656.5
MonotonicityNot monotonic
2021-06-08T21:08:56.038674image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
379.652
 
0.1%
745.062
 
0.1%
533.332
 
0.1%
731.92
 
0.1%
734.942
 
0.1%
3312
 
0.1%
719.781
 
< 0.1%
13375.871
 
< 0.1%
447.641
 
< 0.1%
567.361
 
< 0.1%
Other values (2952)2952
99.5%
ValueCountFrequency (%)
6.21
< 0.1%
13.31
< 0.1%
151
< 0.1%
36.561
< 0.1%
451
< 0.1%
ValueCountFrequency (%)
279138.021
< 0.1%
259657.31
< 0.1%
194550.791
< 0.1%
140450.721
< 0.1%
124564.531
< 0.1%

recency_days
Real number (ℝ≥0)

ZEROS

Distinct272
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.30929919
Minimum0
Maximum373
Zeros33
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-06-08T21:08:56.173664image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q111
median31
Q381
95-th percentile242
Maximum373
Range373
Interquartile range (IQR)70

Descriptive statistics

Standard deviation77.76092244
Coefficient of variation (CV)1.209170733
Kurtosis2.776517247
Mean64.30929919
Median Absolute Deviation (MAD)26
Skewness1.798052889
Sum190870
Variance6046.761059
MonotonicityNot monotonic
2021-06-08T21:08:56.300756image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
199
 
3.3%
487
 
2.9%
285
 
2.9%
385
 
2.9%
876
 
2.6%
1067
 
2.3%
966
 
2.2%
766
 
2.2%
1764
 
2.2%
1655
 
1.9%
Other values (262)2218
74.7%
ValueCountFrequency (%)
033
 
1.1%
199
3.3%
285
2.9%
385
2.9%
487
2.9%
ValueCountFrequency (%)
3732
0.1%
3724
0.1%
3711
 
< 0.1%
3681
 
< 0.1%
3664
0.1%

qtde_invoices
Real number (ℝ≥0)

Distinct56
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.724393531
Minimum1
Maximum206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-06-08T21:08:56.435035image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile17
Maximum206
Range205
Interquartile range (IQR)4

Descriptive statistics

Standard deviation8.857759893
Coefficient of variation (CV)1.547370886
Kurtosis190.7862392
Mean5.724393531
Median Absolute Deviation (MAD)2
Skewness10.76555481
Sum16990
Variance78.45991032
MonotonicityNot monotonic
2021-06-08T21:08:56.562019image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2784
26.4%
3499
16.8%
4393
13.2%
5237
 
8.0%
1190
 
6.4%
6173
 
5.8%
7138
 
4.6%
898
 
3.3%
969
 
2.3%
1055
 
1.9%
Other values (46)332
11.2%
ValueCountFrequency (%)
1190
 
6.4%
2784
26.4%
3499
16.8%
4393
13.2%
5237
 
8.0%
ValueCountFrequency (%)
2061
< 0.1%
1991
< 0.1%
1241
< 0.1%
971
< 0.1%
912
0.1%

qtde_items
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1670
Distinct (%)56.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1582.104447
Minimum1
Maximum196844
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-06-08T21:08:56.701396image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile102.35
Q1296
median640
Q31399.5
95-th percentile4403.25
Maximum196844
Range196843
Interquartile range (IQR)1103.5

Descriptive statistics

Standard deviation5705.291445
Coefficient of variation (CV)3.60614083
Kurtosis516.7418024
Mean1582.104447
Median Absolute Deviation (MAD)421
Skewness18.73765362
Sum4695686
Variance32550350.48
MonotonicityNot monotonic
2021-06-08T21:08:56.844392image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31011
 
0.4%
1509
 
0.3%
889
 
0.3%
2888
 
0.3%
848
 
0.3%
2608
 
0.3%
2728
 
0.3%
2468
 
0.3%
2007
 
0.2%
2197
 
0.2%
Other values (1660)2885
97.2%
ValueCountFrequency (%)
11
< 0.1%
22
0.1%
122
0.1%
161
< 0.1%
171
< 0.1%
ValueCountFrequency (%)
1968441
< 0.1%
802631
< 0.1%
773731
< 0.1%
699931
< 0.1%
645491
< 0.1%

qtde_products
Real number (ℝ≥0)

Distinct468
Distinct (%)15.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean122.7644879
Minimum1
Maximum7838
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-06-08T21:08:57.004928image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q129
median67
Q3135
95-th percentile382
Maximum7838
Range7837
Interquartile range (IQR)106

Descriptive statistics

Standard deviation269.9329358
Coefficient of variation (CV)2.198786803
Kurtosis354.7788412
Mean122.7644879
Median Absolute Deviation (MAD)44
Skewness15.7061352
Sum364365
Variance72863.78981
MonotonicityNot monotonic
2021-06-08T21:08:57.148018image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2843
 
1.4%
2037
 
1.2%
3535
 
1.2%
2935
 
1.2%
1934
 
1.1%
1533
 
1.1%
1132
 
1.1%
2631
 
1.0%
2730
 
1.0%
2530
 
1.0%
Other values (458)2628
88.5%
ValueCountFrequency (%)
16
 
0.2%
214
0.5%
315
0.5%
417
0.6%
526
0.9%
ValueCountFrequency (%)
78381
< 0.1%
56731
< 0.1%
50951
< 0.1%
45801
< 0.1%
26981
< 0.1%

avg_ticket
Real number (ℝ≥0)

SKEWED
UNIQUE

Distinct2968
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.99425671
Minimum2.150588235
Maximum4453.43
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-06-08T21:08:57.297932image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum2.150588235
5-th percentile4.915887985
Q113.11811111
median17.95344712
Q324.98179365
95-th percentile90.052125
Maximum4453.43
Range4451.279412
Interquartile range (IQR)11.86368254

Descriptive statistics

Standard deviation119.5320656
Coefficient of variation (CV)3.622814318
Kurtosis812.9647397
Mean32.99425671
Median Absolute Deviation (MAD)5.979018644
Skewness25.1569664
Sum97926.95393
Variance14287.91471
MonotonicityNot monotonic
2021-06-08T21:08:57.436058image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17.492758621
 
< 0.1%
15.413636361
 
< 0.1%
18.150615381
 
< 0.1%
17.943444441
 
< 0.1%
43.21921
 
< 0.1%
33.535714291
 
< 0.1%
9.4182926831
 
< 0.1%
19.557670451
 
< 0.1%
132.07389831
 
< 0.1%
16.807222221
 
< 0.1%
Other values (2958)2958
99.7%
ValueCountFrequency (%)
2.1505882351
< 0.1%
2.43251
< 0.1%
2.4623711341
< 0.1%
2.5112413791
< 0.1%
2.5153333331
< 0.1%
ValueCountFrequency (%)
4453.431
< 0.1%
3202.921
< 0.1%
1687.21
< 0.1%
952.98751
< 0.1%
872.131
< 0.1%

avg_recency_days
Real number (ℝ≥0)

Distinct1258
Distinct (%)42.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.30213285
Minimum1
Maximum366
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-06-08T21:08:57.578433image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q125.91730769
median48.26785714
Q385.33333333
95-th percentile200.65
Maximum366
Range365
Interquartile range (IQR)59.41602564

Descriptive statistics

Standard deviation63.50535844
Coefficient of variation (CV)0.9435861206
Kurtosis4.908048776
Mean67.30213285
Median Absolute Deviation (MAD)26.26785714
Skewness2.066084007
Sum199752.7303
Variance4032.93055
MonotonicityNot monotonic
2021-06-08T21:08:57.719382image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1425
 
0.8%
422
 
0.7%
7021
 
0.7%
720
 
0.7%
3519
 
0.6%
4918
 
0.6%
2117
 
0.6%
4617
 
0.6%
1117
 
0.6%
516
 
0.5%
Other values (1248)2776
93.5%
ValueCountFrequency (%)
116
0.5%
1.51
 
< 0.1%
213
0.4%
2.51
 
< 0.1%
2.6013986011
 
< 0.1%
ValueCountFrequency (%)
3661
< 0.1%
3651
< 0.1%
3631
< 0.1%
3621
< 0.1%
3572
0.1%

frequency
Real number (ℝ≥0)

SKEWED

Distinct1225
Distinct (%)41.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1138323742
Minimum0.005449591281
Maximum17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-06-08T21:08:57.878203image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0.005449591281
5-th percentile0.008893504781
Q10.01633986928
median0.02589835169
Q30.04947858264
95-th percentile1
Maximum17
Range16.99455041
Interquartile range (IQR)0.03313871336

Descriptive statistics

Standard deviation0.4082205551
Coefficient of variation (CV)3.586155151
Kurtosis989.0663249
Mean0.1138323742
Median Absolute Deviation (MAD)0.0121968864
Skewness24.87687084
Sum337.8544866
Variance0.1666440216
MonotonicityNot monotonic
2021-06-08T21:08:58.014089image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1198
 
6.7%
0.062518
 
0.6%
0.0277777777817
 
0.6%
0.0238095238116
 
0.5%
0.0833333333315
 
0.5%
0.0909090909115
 
0.5%
0.0344827586214
 
0.5%
0.0294117647114
 
0.5%
0.0357142857113
 
0.4%
0.0256410256413
 
0.4%
Other values (1215)2635
88.8%
ValueCountFrequency (%)
0.0054495912811
< 0.1%
0.0054644808741
< 0.1%
0.0054794520551
< 0.1%
0.0054945054951
< 0.1%
0.0055865921792
0.1%
ValueCountFrequency (%)
171
 
< 0.1%
31
 
< 0.1%
26
 
0.2%
1.1428571431
 
< 0.1%
1198
6.7%

qtde_returns
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct213
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.88847709
Minimum0
Maximum9014
Zeros1481
Zeros (%)49.9%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-06-08T21:08:58.164089image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q39
95-th percentile100
Maximum9014
Range9014
Interquartile range (IQR)9

Descriptive statistics

Standard deviation282.864784
Coefficient of variation (CV)8.107685048
Kurtosis596.2019916
Mean34.88847709
Median Absolute Deviation (MAD)1
Skewness21.9754032
Sum103549
Variance80012.48604
MonotonicityNot monotonic
2021-06-08T21:08:58.309900image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01481
49.9%
1164
 
5.5%
2148
 
5.0%
3105
 
3.5%
489
 
3.0%
678
 
2.6%
561
 
2.1%
1251
 
1.7%
743
 
1.4%
843
 
1.4%
Other values (203)705
23.8%
ValueCountFrequency (%)
01481
49.9%
1164
 
5.5%
2148
 
5.0%
3105
 
3.5%
489
 
3.0%
ValueCountFrequency (%)
90141
< 0.1%
80041
< 0.1%
44271
< 0.1%
37681
< 0.1%
33321
< 0.1%

avg_basket_size
Real number (ℝ≥0)

Distinct1978
Distinct (%)66.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean236.252886
Minimum1
Maximum6009.333333
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-06-08T21:08:58.451566image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile44
Q1103.2375
median172.2916667
Q3281.5480769
95-th percentile599.58
Maximum6009.333333
Range6008.333333
Interquartile range (IQR)178.3105769

Descriptive statistics

Standard deviation283.8931966
Coefficient of variation (CV)1.201649645
Kurtosis102.7816879
Mean236.252886
Median Absolute Deviation (MAD)83.04166667
Skewness7.701877717
Sum701198.5657
Variance80595.34706
MonotonicityNot monotonic
2021-06-08T21:08:58.585557image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10011
 
0.4%
11410
 
0.3%
829
 
0.3%
869
 
0.3%
739
 
0.3%
608
 
0.3%
888
 
0.3%
758
 
0.3%
1368
 
0.3%
1057
 
0.2%
Other values (1968)2881
97.1%
ValueCountFrequency (%)
12
0.1%
21
< 0.1%
3.3333333331
< 0.1%
5.3333333331
< 0.1%
5.6666666671
< 0.1%
ValueCountFrequency (%)
6009.3333331
< 0.1%
42821
< 0.1%
39061
< 0.1%
3868.651
< 0.1%
28801
< 0.1%

avg_unique_basket_size
Real number (ℝ≥0)

Distinct906
Distinct (%)30.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.48997702
Minimum0.2
Maximum259
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-06-08T21:08:58.726653image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile2
Q17.666666667
median13.6
Q322.14464286
95-th percentile46
Maximum259
Range258.8
Interquartile range (IQR)14.47797619

Descriptive statistics

Standard deviation15.46012684
Coefficient of variation (CV)0.8839420902
Kurtosis29.32468467
Mean17.48997702
Median Absolute Deviation (MAD)6.6
Skewness3.436467798
Sum51910.25179
Variance239.015522
MonotonicityNot monotonic
2021-06-08T21:08:58.873542image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1342
 
1.4%
941
 
1.4%
1639
 
1.3%
839
 
1.3%
1438
 
1.3%
1738
 
1.3%
536
 
1.2%
1136
 
1.2%
736
 
1.2%
1535
 
1.2%
Other values (896)2588
87.2%
ValueCountFrequency (%)
0.21
 
< 0.1%
0.253
0.1%
0.33333333336
0.2%
0.41
 
< 0.1%
0.40909090911
 
< 0.1%
ValueCountFrequency (%)
2591
< 0.1%
1771
< 0.1%
1481
< 0.1%
1271
< 0.1%
1051
< 0.1%

Interactions

2021-06-08T21:08:35.447342image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:35.601970image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:35.709550image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:35.860833image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:35.989148image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:36.109259image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:36.224583image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:36.327689image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:36.437206image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:36.544122image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:36.646465image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:36.759430image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:36.872942image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:36.990193image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:37.125398image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:37.272749image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:37.400720image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:37.541169image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:37.679163image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:37.817829image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:37.955736image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:38.086759image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:38.195902image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:38.309101image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:38.420022image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:38.533960image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:38.645187image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:38.756822image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:38.863163image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:38.982453image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:39.102621image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:39.207266image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:39.320704image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:39.430276image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:39.530586image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:39.636481image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:39.743585image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:39.859664image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:39.972370image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:40.088159image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:40.196915image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:40.321050image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:40.440322image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:40.547037image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:40.661198image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:40.776009image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:40.882463image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:42.025785image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:42.153196image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:42.257875image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:42.361285image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:42.463039image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:42.571867image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:42.686451image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:42.801198image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:42.900813image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:43.010404image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:43.122216image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:43.232892image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:43.342885image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:43.451364image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:43.568736image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:43.689986image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:43.811961image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:43.940645image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:44.058087image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:44.186350image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:44.297196image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:44.415482image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
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2021-06-08T21:08:44.644745image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:44.760849image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:44.876330image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:44.992210image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
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2021-06-08T21:08:45.222791image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:45.339300image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:45.450692image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:45.574743image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:45.688313image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:45.824829image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:45.946944image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:46.057703image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:46.177894image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:46.292901image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:46.390064image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:46.490200image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:46.590035image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:46.695138image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:46.793478image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:47.087861image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:47.214304image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:47.324098image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:47.435989image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:47.536421image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:47.642265image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:47.746034image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:47.861778image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:47.978744image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:48.094032image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:48.211636image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:48.326072image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:48.450133image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:48.571616image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:48.684577image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:48.811132image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:48.922229image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:49.042272image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:49.168387image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:49.289304image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:49.400132image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:49.511586image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:49.624833image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:49.731354image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:49.850163image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:49.966529image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:50.079116image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:50.196606image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:50.308098image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:50.420771image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:50.531796image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:50.632381image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:50.731285image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:50.830408image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:50.932147image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:51.029297image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:51.136588image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:51.243017image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:51.339476image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:51.443208image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:51.547876image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:51.649442image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:51.755149image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:51.867513image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:51.982482image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:52.094935image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:52.210701image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:52.321903image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:52.442749image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:52.562673image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:52.675290image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:52.794748image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:52.911645image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:53.022768image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:53.360713image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:53.488682image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:53.603257image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:53.713835image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:53.834007image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:53.940738image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:54.059876image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:54.200880image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:54.324882image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:54.437029image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:54.544339image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-08T21:08:54.644738image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Correlations

2021-06-08T21:08:59.010875image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-06-08T21:08:59.215514image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-06-08T21:08:59.414841image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-06-08T21:08:59.922565image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-06-08T21:08:54.898872image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
A simple visualization of nullity by column.
2021-06-08T21:08:55.171904image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexcustomer_idgross_revenuerecency_daysqtde_invoicesqtde_itemsqtde_productsavg_ticketavg_recency_daysfrequencyqtde_returnsavg_basket_sizeavg_unique_basket_size
00178505391.21372.034.01733.0297.018.15222235.50000017.00000040.050.9705880.617647
11130473232.5956.09.01390.0171.018.90403527.2500000.02830235.0154.44444411.666667
22125836705.382.015.05028.0232.028.90250023.1875000.04032350.0335.2000007.600000
3313748948.2595.05.0439.028.033.86607192.6666670.0179210.087.8000004.800000
4415100876.00333.03.080.03.0292.0000008.6000000.07317122.026.6666670.333333
55152914623.3025.014.02102.0102.045.32647123.2000000.04011529.0150.1428574.357143
66146885630.877.021.03621.0327.017.21978618.3000000.057221399.0172.4285717.047619
77178095411.9116.012.02057.061.088.71983635.7000000.03352041.0171.4166673.833333
881531160767.900.091.038194.02379.025.5434644.1444440.243316474.0419.7142866.230769
99160982005.6387.07.0613.067.029.93477647.6666670.0243900.087.5714294.857143

Last rows

df_indexcustomer_idgross_revenuerecency_daysqtde_invoicesqtde_itemsqtde_productsavg_ticketavg_recency_daysfrequencyqtde_returnsavg_basket_sizeavg_unique_basket_size
29585626177271060.2515.01.0645.066.016.0643946.01.0000006.0645.00000066.000000
2959563617232421.522.02.0203.036.011.70888912.00.1538460.0101.50000015.000000
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